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We first aimed to identify sample outliers that exhibited extreme metabolite profiles as a consequence of analytical error.
Clinical trials in humans are carefully designed investigations executed with consideration of critical methodological issues, such as pre-specified entrance criteria and validated, outcome measures coupled with power analysis to identify sample size.
Using these slopes as predictors for the Random Forest allowed the algorithm to identify sample gases with a high degree of accuracy (Additional files 1, 2).
To further explore crucial metabolites involved in altering the P. citrinum W1 metabolome in response to exogenous fructose, OPLS-DA was used to identify sample patterns.
In order to identify sample site clustering, factor scores obtained from PCA are used and PC1 vs. PC2 and PC1 vs. PC3 plots are generated (Figs. 7 and 8).
Its chemical characteristics identify sample PEP-093 clearly as a lump of black obsidian (e.g. [42]).
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Researchers and laboratory personnel had no access to identifiable information and could identify samples by number only.
Researchers and laboratory personnel did not have access to identifiable information, and could only identify samples by number.
MacBane, a dog at the Penn Vet Working Dog Centre, can identify samples of tissue containing ovarian cancer.
The two cases and multiple cases modules allow users to identify sample-specific and common variations.
This comparison allowed us to further assess data quality and to identify sample-handling errors.
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CEO of Professional Science Editing for Scientists @ prosciediting.com